Abstract
Energy management of a virtual power plant (VPP) that consists of wind farm (WF), energy storage systems and a demand response program is discussed in the present study. The introduced ...strategy is realized at the electrical power transmission level and takes into account the collaboration between VPPs in day‐ahead energy and reserve markets. One notable feature of the proposed strategy is attempting to make the revenue of VPPs close to the operating cost of generating units as much as possible. The objective function is subjected to the network‐constrained unit commitment model, up and down reserve requirements and the proposed VPP constraints. This method is taking into account the uncertainty in system and VPP loads, day‐ahead market energy and reserve price and WF power generation. This strategy is applied as hybrid stochastic‐robust scheduling to the VPP at the electrical power transmission level, where the scenario‐based stochastic programming models the uncertainty of day‐ahead market prices, and the bounded uncertainty‐based robust optimization has been adopted to model the uncertainties related to the load and WF power. Scheme has been tested on IEEE systems. According to the obtained results, the proposed coordination of VPPs in the mentioned markets demonstrates capability of suggested strategy.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
This paper presents a method for coordinated network expansion planning (CNEP) in which the difference between the total cost and the flexibility benefit is minimized. In the proposed method, the ...generation expansion planning (GEP) of wind farms is coordinated with the transmission expansion planning (TEP) problem by using energy storage systems (ESSs) to improve network flexibility. To consider the impact of the reactive power in the CNEP problem, the AC power flow model is used. The CNEP constraints include the AC power flow equations, planning constraints of the different equipment, and the system operating limits. Therefore, this model imposes hard nonlinearity onto the problem, which is linearized by the use of first-order Taylor’s series and the big-M method as well as the linearization of the circular plane. The uncertainty of loads, the energy price, and the wind farm generation are modeled by scenario-based stochastic programming (SBSP). To determine the effectiveness of the proposed solution approach, it is tested on the IEEE 6-bus and 24-bus test systems using GAMS software.
High voltage gain DC-DC boost converters are widely used in grid-connected applications through integration with the Renewable Energy Sources (RESs). Photovoltaic (PV) arrays or Fuel Cells (FCs) ...generate a limited value of the DC voltages and then for high power and high voltage applications, at the first stage, these voltages should be increased. This study presents a high gain, single-switched, and efficient DC-DC boost converter using the switched-capacitor and switched-inductor cells. These blocks easily can enhance the voltage and present an input current with the least values of the ripples. This will be done through replacing the location of the input inductors and by applying a switched-inductor block. Magnetizing in parallel and demagnetizing in series for the inductors present the smaller input current stresses. A single switch is used for the proposed boost converter that directly decreases the complexity of the control circuit for obtaining a fixed DC voltage at the output side for flexible input voltages or loads. More voltages will be presented by the used switched-capacitor cell simply by adding several diodes and capacitors. A deep and detailed mathematical analysis will be presented for continuous (CCM) and discontinuous conduction modes (DCM) and a 200 W laboratory-scaled prototype is presented. The results of the hardware tests confirm the correctness of the theoretical analysis and simulation results.
This study presents the optimal model of the coordinated flexible energy and self‐healing management (C‐FE&SH‐M) in the active distribution network (ADN) including renewable energy sources (RESs), ...electric vehicles (EVs) and demand response program (DRP).The flexible energy management (FEM) is extracted using coordination between the RESs, EVs and DRP. The self‐healing method (SHM) is related to multi‐agent system‐based restoration process (MAS‐based RP) that finds the optimal restoration pattern at the fault condition according to the different zone agents (ZAs) distributing along with the network. This method minimizes the difference between energy cost and flexibility benefit related to the FEM part and difference between the number of switching operation and priority loads restored based on the SHM part. Also, this problem subjects to power flow equations, RESs and active loads constraints, restoration process formulation and system operation limits. Stochastic programming is used to model the uncertainty of loads, energy prices, RESs and EVs. Hereupon, the suggested strategy is implemented on the 33‐bus radial distribution network and it is solved by the crow search algorithm (CSA). Ultimately, the obtained results imply the high flexibility and security of the operation, incorporating the proposed strategy, and delineate the optimal restoration scheme for the ADN.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
This paper investigates a multi-objective optimal energy planning strategy for a hub, incorporating renewable and non-renewable resources, like PV, tidal turbine, fuel-cell, CHP, boiler, ...micro-turbine, reactor, reformer, electrolyzer, and energy storage by utilizing the time of use program (TOU). In this strategy, tidal turbine, fuel-cell, and reformer technologies are considered novel technologies that simultaneously reduce the proposed hub’s cost and pollution. The hub’s total cost and pollution are considered objective functions. To make the results more realistic, characteristics of the tidal turbine are investigated by utilizing the manufactory’s company information. The problem is then modeled as real mixed integer programming (RMIP) and is solved in GAMS software using a CPLEX solver. Epsilon constraints method and fuzzy satisfying approach are used to select the optimal solution based on the proposed model. Finally, a sensitivity analysis is performed to assess the effective parameters that affect the planning’s results. The results show that the overall pollution is reduced by about 9% by assuming the proposed planning, and the total profit is increased by about 30%.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper outlines the operation of a Smart Distribution Network (SDN) that couples a Virtual Power Plant and Electric Springs (CVEs). In fact, CVEs participate simultaneously in energy and reactive ...service markets. The prime aim of the proposed scheme is to maximize the predicted profits of CVEs in the mentioned markets. The constraints in the problem formulation are the AC optimal power flow equations, flexibility limits in the network, and the operating model of CVEs. Further, the design is in a nonlinear formulation, which is followed by a linear approximation model to access a unique optimal response. Stochastic optimization is used to account for uncertainties in energy price, load, renewable power, and energy consumption of mobile storage devices. In addition, the results from implementing the design on the IEEE 69-bus SDN confirm the potential of CVEs to enhance the network's operation and access significant profits for power sources, storage devices, and responsive load. Finally, the design achieved 100% flexibility for the SDN through proper management of CVEs, resulting in an improvement of operating indices between 15-97% compared to power flow studies. Moreover, the CVEs profit in the modeling of uncertainties reduces approximately 19.6% compared to the deterministic model of the proposed scheme under complete flexibility conditions.
With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a ...need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
This paper presents the energy management of smart distribution network including integrated system of hydrogen storage and renewable sources. Objective is to assess economic, operation, flexibility, ...and reliability goals of the distribution system operator. Objective function minimizes costs of operation, reliability, energy losses, and network flexibility. Scheme is constrained by AC optimal power flow equations, network reliability limitation, and integrated system model. Scheme utilizes scenario-based stochastic optimization to model of uncertainties, such as load, parameters of renewable resources, energy prices, and the availability of network equipment. Novelty is modeling and performance evaluation of the proposed integrated system as a type of flexibility resource in the operation of distribution systems proportional to the economic, operation, flexibility, and reliability objectives of the network operator. Numerical results demonstrate energy management capabilities of the discussed integrated energy system, which contribute to enhancing economic and technical conditions of distribution network. The inclusion of hydrogen storage in the renewable integrated energy system has been found to enhance the economic viability, operational efficiency, and reliability of the distribution network by around 46.8%, 41%–53%, and 95%, respectively, when compared to network power flow. Aforementioned integrated system demonstrates 100% flexibility through the utilization of hydrogen storage.
•Developing a model for assessing the efficiency and effectiveness of hydrogen storage and bio-waste units farm.•Considering an integrated energy system to evaluate the economic and technical objectives of the DSO.•The utilization of an integrated hydrogen storage and RESs system as a means to enhance the network flexibility.•Simultaneous modeling of economic, operation, reliability, and flexibility objectives of the DSO.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Regardless of the application in which power electronic converters are deployed, their desired performances crucially depend on the controlling strategy while different impressive parameters are ...varied. This paper offers a novel controlling strategy originated from the mixture of two well-known controlling techniques, namely feedback (FBC) and model predictive (MPC) controllers. It uses the advantages of the above-mentioned controllers while their drawbacks or limitations are covered by each other using the mixture of experts (MoE) technique. Two neural networks for capturing the features of MPC and FBC along with a gating network as the main tool of MoE are employed in order to optimize the controlling of the DC-DC power electronic converters. These networks are trained through a set of pair data as the input vector and the target data. The results reveal that better performance can be obtained via benefit exploitation of both controlling techniques using a comprehensive MoE. The dynamic and steady state errors are decreased by 5% and 8%, respectively which demonstrate a global enhancement in the controlling of the DC-DC power electronic converters.
This paper evaluates the voltage security of the distribution networks in the presence of electric vehicles in the optimization framework. Accordingly, the main objective functions of this ...optimization problem include maximization of voltage security margin and minimization of operational cost. Also, it is supposed that the electric vehicles are equipped with bidirectional chargers to control active and reactive power in smart distribution networks, simultaneously. The objective functions are subject to the constraints of power flow equations, system operating limits and electric vehicle constraints. The proposed model is implemented on the 33-bus distribution network to evaluate the performance of the proposed optimization scheme for the management of the smart distribution networks in the presence of electric vehicles. The results show that the operational cost and network voltage security margin are reduced in the case of the higher electric vehicle penetration rate when electric vehicles are used for charging active power and reactive power control capability, with respect to the case that does not include electric vehicles.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
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